code: def zipf(
      random_state, a, size=None, chunk_size=None, gpu=None, dtype=None
  ):
      r"""
      Draw samples from a Zipf distribution.
  
      Samples are drawn from a Zipf distribution with specified parameter
      `a` > 1.
  
      The Zipf distribution (also known as the zeta distribution) is a
      continuous probability distribution that satisfies Zipf's law: the
      frequency of an item is inversely proportional to its rank in a
      frequency table.
  
      Parameters
      ----------
      a : float or array_like of floats
          Distribution parameter. Should be greater than 1.
      size : int or tuple of ints, optional
          Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
          ``m * n * k`` samples are drawn.  If size is ``None`` (default),
          a single value is returned if ``a`` is a scalar. Otherwise,
          ``mt.array(a).size`` samples are drawn.
      chunk_size : int or tuple of int or tuple of ints, optional
          Desired chunk size on each dimension
      gpu : bool, optional
          Allocate the tensor on GPU if True, False as default
      dtype : data-type, optional
        Data-type of the returned tensor.
  
      Returns
      -------
      out : Tensor or scalar
          Drawn samples from the parameterized Zipf distribution.
  
      See Also
      --------
      scipy.stats.zipf : probability density function, distribution, or
          cumulative density function, etc.
  
      Notes
      -----
      The probability density for the Zipf distribution is
  
      .. math:: p(x) = \frac{x^{-a}}{\zeta(a)},
  
      where :math:`\zeta` is the Riemann Zeta function.
  
      It is named for the American linguist George Kingsley Zipf, who noted
      that the frequency of any word in a sample of a language is inversely
      proportional to its rank in the frequency table.
  
      References
      ----------
      .. [1] Zipf, G. K., "Selected Studies of the Principle of Relative
             Frequency in Language," Cambridge, MA: Harvard Univ. Press,
             1932.
  
      Examples
      --------
      Draw samples from the distribution:
  
      >>> import mars.tensor as mt
  
      >>> a = 2. # parameter
      >>> s = mt.random.zipf(a, 1000)
  
      Display the histogram of the samples, along with
      the probability density function:
  
      >>> import matplotlib.pyplot as plt
      >>> from scipy import special
  
      Truncate s values at 50 so plot is interesting:
  
      >>> count, bins, ignored = plt.hist(s[s<50].execute(), 50, normed=True)
      >>> x = mt.arange(1., 50.)
      >>> y = x**(-a) / special.zetac(a)
      >>> plt.plot(x.execute(), (y/mt.max(y)).execute(), linewidth=2, color='r')
      >>> plt.show()
      """
      if dtype is None:
          dtype = (
              np.random.RandomState()
              .zipf(handle_array(a), size=(0,))
              .dtype
          )
  
      size = random_state._handle_size(size)
      seed = gen_random_seeds(1, random_state.to_numpy())[0]
      op = TensorZipf(size=size, seed=seed, gpu=gpu, dtype=dtype)
      return op(a, chunk_size=chunk_size)
  
file_path: mars/mars/tensor/random/zipf.py
imports: import numpy
  from ...None import opcodes
  from ...serialization.serializables import AnyField
  from ..utils import gen_random_seeds
  from .core import TensorRandomOperandMixin, handle_array, TensorDistribution
label_desc: Use image node_id 1 for calling a global function with example usage: zipf(random_state, a, size, chunk_size, gpu, dtype) and returns: op